Hierarchical context in risk assessment using machine learning

ABSTRACT

Methods, systems, and apparatus for receiving a request for a risk assessment for a parcel, receiving a set of images for the parcel, the set of images including two or more images, each image having an image scale and an image resolution that is different from other images in the set of images, providing a first-level feature embedding and a second-level feature embedding, the first-level feature embedding being provided by processing a first-level image through a first-level machine learning (ML) model, and the second-level feature embedding being provided by processing a second-level image through a second-level ML model, determining a risk assessment at least partially by processing each of the first-level feature embedding and a second-level feature embedding through a fusion network, and providing a representation of the risk assessment for display.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Prov. App. No. 63/265,051,filed on Dec. 7, 2021, the disclosure of which is expressly incorporatedherein by reference in the entirety.

TECHNICAL FIELD

This specification relates generally to geospatial predictions of risk,and more specifically to providing hierarchical context in riskassessments using machine learning.

BACKGROUND

Adverse events, such as natural disasters are increasing in bothfrequency and intensity. Example natural disasters can includewildfires, hurricanes, tornados, and floods, among several others.Natural disasters often result in significant loss that can include aspectrum of economic losses, property losses, and physical losses (e.g.,deaths, injuries). Consequently, significant time and effort is expendednot only predicting occurrences of natural disasters, butcharacteristics of natural disasters such as duration, severity, spread,and the like. Technologies, such as machine learning (ML), have beenleveraged to generate predictions around natural disasters. However,natural disasters present a special use case for predictions using MLmodels, which results in technical problems that must be addressed togenerate reliable and actionable predictions.

SUMMARY

This specification describes systems, methods, devices, and othertechniques relating to using machine learning (ML) models in a riskassessment system. More particularly, innovative aspects of the subjectmatter described in this specification relate to a ML system thatincludes multiple ML models to predict risk based on respective imagescales and image resolutions, each ML model providing a featureembedding. Each feature embedding encodes a spatial context andresolution of a respective image scale. The feature embeddings areprovided as input to a fusion network, which processes the featureembeddings to provide a risk assessment. In some examples, the riskassessment is provided as a score that represents a likelihood ofoccurrence of an adverse event.

In general, innovative aspects of the subject matter described in thisspecification can include actions of receiving a request for a riskassessment for a parcel, receiving a set of images for the parcel, theset of images including two or more images, each image having an imagescale and an image resolution that is different from other images in theset of images, providing a first-level feature embedding and asecond-level feature embedding, the first-level feature embedding beingprovided by processing a first-level image through a first-level machinelearning (ML) model, and the second-level feature embedding beingprovided by processing a second-level image through a second-level MLmodel, determining a risk assessment at least partially by processingeach of the first-level feature embedding and a second-level featureembedding through a fusion network, and providing a representation ofthe risk assessment for display. Other implementations of this aspectinclude corresponding systems, apparatus, and computer programs,configured to perform the actions of the methods, encoded on computerstorage devices.

These and other implementations can each optionally include one or moreof the following features: the first-level feature embedding includes aparcel-level feature embedding that is generated at least partiallybased on a parcel-level image in the set of images, and the second-levelfeature embedding includes one of a neighborhood-level feature embeddingthat is generated at least partially based on a neighborhood-level imagein the set of images and a landscape-level feature embedding that isgenerated at least partially based on a landscape-level image in the setof images; the parcel-level image has an image scale and an imageresolution that are greater than an image scale and the image resolutionof each of the neighborhood-level image and the landscape-level image;each image in the set of images represents the parcel within a thresholdof time from a designated time associated with the request; at least oneimage in the set of images includes an overhead view of the parcel andat least one other image in the set of images includes one of avegetation segmentation map and an elevation map; actions furtherinclude providing a third-level feature embedding by processing athird-level image through a third-level ML model, wherein thethird-level feature embedding is processed through the fusion network indetermining the risk assessment; determining the risk assessmentincludes converting a relative risk prediction to the risk assessmentusing a calibration curve; the relative risk prediction is output by alast layer of the fusion network; the first-level feature embedding isoutput from a non-final layer of the first-level ML model and thesecond-level feature embedding is output from a non-final layer of thesecond-level ML model; and the first-level feature embedding is furtherprovided by processing a set of first-level pixel data through thefirst-level ML model, and the second-level feature embedding is furtherprovided by processing a set of second-level pixel data through thesecond-level ML model.

The present disclosure also provides a non-transitory computer-readablestorage medium coupled to one or more processors and having instructionsstored thereon which, when executed by the one or more processors, causethe one or more processors to perform operations in accordance withimplementations provided herein.

It is appreciated that the methods and systems in accordance with thepresent disclosure can include any combination of the aspects andfeatures described herein. That is, methods and systems in accordancewith the present disclosure are not limited to the combinations ofaspects and features specifically described herein, but also include anycombination of the aspects and features provided.

The details of one or more implementations of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other features, aspects, andadvantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a risk assessment system in accordance withimplementations of the present disclosure.

FIGS. 2A-2C depict images of an example property in accordance withimplementations of the present disclosure.

FIG. 3 is a flow diagram of an example process that can be executed inaccordance with implementations of the present disclosure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Implementations of the present disclosure are directed to using machinelearning (ML) models in a risk assessment system. More particularly,innovative aspects of the subject matter described in this specificationrelate to a ML system that includes multiple ML models to predict riskbased on respective image scales and image resolutions, each ML modelproviding a feature embedding. Each feature embedding encodes a spatialcontext and resolution of a respective image scale. The featureembeddings are provided as input to a fusion network, which processesthe feature embeddings to provide a risk assessment. In some examples,the risk assessment is provided as a score that represents a likelihoodof occurrence of an adverse event. In some implementations, geospatialpredictions of risk (e.g., risk assessments) provided by the ML systemof the present disclosure can be used when determining a course ofaction. For example, one or more preemptive mitigation actions can bedetermined and performed to reduce the likelihood that the adverse eventwill occur.

For purposes of illustration, implementations of the present disclosureare described in further detail herein with reference to an example riskassessment in view of an example adverse event. The example riskassessment includes a risk to a parcel that can include real propertyand one or more assets located thereon (e.g., home, building). This isgenerally referred to herein as property risk or parcel risk. An exampleadverse event includes wildfire. For example, and using the illustrativeexamples, a property risk (risk assessment) is provided as a score thatrepresents a likelihood of a parcel being damaged by a wildfire. It iscontemplated, however, that implementations of the present disclosurecan be realized using any appropriate asset and any appropriate adverseevent.

To provide context for implementations of the present disclosure,systems have been developed to assess property risk in an automated orsemi-automated manner. For example, risk assessment systems have beendeveloped that can process images of parcels using a ML model todetermine risk assessments. However, property risk can come from variousaspects at multiple scales. For example, objects (e.g., buildings,brush, trees, forests, waterways, lakes) can influence risk to a parcel.At the same time, objects span different scales. For example, a shed,vegetation, and/or stack of firewood located on a parcel is at a muchsmaller scale than a forest or lake located near the parcel. Recognizingobjects at different scales is a fundamental challenge in computervision.

As a result, some traditional risk assessment systems use techniquessuch as feature pyramids, which can be described as scale-invariant.This means that a scale change of an object is offset by shifting alevel of the object within a feature pyramid. This enables a ML model todetect objects across a range of scales by inputting positions andpyramid levels to the ML model. However, pyramid features are processor-and memory-intensive in both training and inference. While developmentin pyramid features has provided some improvements, the improvementshave been marginal and/or come with other technical costs.

In view of this, and as introduced above, implementations of the presentdisclosure provide a ML system that includes multiple ML models and afusion network to predict risk to a parcel over a hierarchy of imagescales and image resolutions. In some examples, image scale (alsoreferred to as map scale) indicates a relative difference in size (ordistance) between the image and real-world features actually depicted inthe image. In some examples, image scale is provided as a ratio (alsoreferred to as fractional scale) of image distance to ground distance.For example, an image scale of 1:100,000 indicates that 1 cm on theimage equals 100,000 cm (1 km) in the real-world entity (e.g., ground)captured in the image. Accordingly, image scale represents the relativesize of real-world features depicted in images. In some examples, imageresolution (also referred to as spatial resolution) is an indication ofthe size of a pixel of an image in terms of dimensions of real-worldfeatures (e.g., ground). Image resolution can be provided as a singlevalue that indicates the length of a side of a square. For example, animage resolution of 30 m indicates that one pixel represents an area of30 m×30 m (900 m²) on the ground.

In some examples, data density can be represented as a number of datapoints for an area within a geospatial region. For example, for atwo-dimensional image of a geospatial region, a data density can beprovided as a number of data points per m². A high data densityindicates that there are relatively more data points per unit area of ageospatial region than a low data density. Different data densities canbe appropriate for different metrics. For example, lower density datalayers can allow an ML model to learn characteristics of wildfires thatare likely to reach a region, such as elevation or previous prescribedburns. Higher density data can help the ML model learn finer-grainedinformation about a region, for example, that the upwind direction of aregion (e.g., a neighborhood) had a prescribed burn last year.

As noted above, property risk can come from various aspects at multiplescales. At a granular level (e.g., parcel context), property risk candepend on risk factors such as materials that an asset located on aparcel is constructed from, as well as objects located on the parcel(e.g., vegetation) and spatial arrangement and/or density of objects.For example, property risk can depend on roof material (e.g., thatched,shingle, tile, metal) and structural material (e.g., wood, brick,cement). These types of risk factors can be identified with relativelysmaller spatial context (e.g., parcel-level), but it requires relativelyhigh-resolution imagery (e.g., 10 cm-5 m per pixel).

At a less granular level (e.g., neighborhood context), property risk candepend on risk factors such as materials of other assets and/or otherfeatures in a neighborhood of the asset. A neighborhood of a propertycan include, for example, a radius of X (e.g., 50 m) around the parcel,or a rectangle of Y by Z (e.g., 50 m×50 m) around the parcel. This caninclude, for example, assets and/or objects located on neighboringparcels, as well as vegetation, roadways, water features, and the likelying within the neighborhood. This can also include spatial arrangementand/or density of objects and/or other features located on neighboringparcels. These types of risk factors can be identified within somespatial context (e.g., neighborhood-level) and requires more moderateresolution imagery (e.g., 10 m-30 m per pixel).

At a more abstract level (e.g., landscape-level), property risk candepend on risk factors of the landscape. This can include, for example,elevation (e.g., whether the property sits on a slope), vegetation(e.g., whether the property is close to forests, whether there areprescribed burns in the area), and water bodies (e.g., nearby lakes,rivers, reservoirs). This information helps to inform on characteristicsof wildfires that are likely to affect likelihood of damage to theparcel. These types of risk factors can be identified with more spatialcontext (e.g., landscape context) within the broader landscapesurrounding the parcel. For example, a landscape for the parcel canencompass a radius of Q (e.g., 5 kilometers (km)) around the property,or a rectangle of R by S (e.g., 5 km×5 km) around the property. Here,relatively low-resolution imagery is used (e.g., ≥60 m per pixel).

In accordance with implementations of the present disclosure, each MLmodel of the ML system is associated with a respective image scale andimage resolution. In some implementations, a parcel-level ML modelprocesses parcel-level images having a relatively large image scale(e.g., 1:50-1:500, measured in cm) and a relatively high imageresolution (e.g., 10 cm-5 m per pixel), a neighborhood-level ML modelprocesses neighborhood-level images having a relatively moderate imagescale (e.g., 1:1500-1:2500, measured in cm) and a relatively mediumimage resolution (e.g., 10 m-30 m per pixel), and a landscape-level MLmodel processes landscape-level images having a relatively small imagescale (e.g., 1:10,000-1:30,000, measured in cm) and a relatively lowimage resolution (e.g., ≥60 m per pixel). In some examples, theparcel-level ML model provides a parcel-level embedding that encodes aspatial context and resolution of a parcel-level image, theneighborhood-level ML model provides a neighborhood-level embedding thatencodes a spatial context and resolution of a neighborhood-level image,and the landscape-level ML model provides a landscape-level embeddingthat encodes a spatial context and resolution of a landscape-levelimage. Each of the feature embeddings is provided as a multi-dimensionalvector.

It can be noted that disparate resolution levels provide technicaladvantages. For example, for lower resolutions, the respective ML modelscan have relatively high accuracies for their respective contexts,because there is less noise in feature sets that are represented inimages of decreasing resolution. Further, smaller-sized datasets can beused for training the ML models that process lower resolution images. Inthis manner, technical improvements are achieved in terms of processorsand memory expended during training, while enabling the disparatecontexts to contribute to assessing property risk.

In some examples, the feature embeddings include the same number ofdimensions. In some examples, the feature embeddings include differentnumbers of dimensions. In some examples, the parcel-level embedding isprovided as a multi-dimensional vector that encodes features at theparcel-level (e.g., buildings on a parcel, materials building is madefrom, features in yard). In some examples, the neighborhood-levelembedding is provided as a multi-dimensional vector that encodesfeatures at the neighborhood-level (e.g., materials surroundingbuildings are made from, features in yards of surrounding buildings,features between yards). In some examples, the landscape-level embeddingis provided as a multi-dimensional vector that encodes features at thelandscape-level (e.g., roadways, rivers, lakes, reservoirs, forests inbroader surrounding area).

In accordance with implementations of the present disclosure, theparcel-level embedding, the neighborhood-level embedding, and thelandscape-level embedding are provided as input to the fusion network.The fusion network processes the parcel-level embedding, theneighborhood-level embedding, and the landscape-level embedding toprovide a risk assessment. In the non-limiting example of the presentdisclosure, the risk assessment is provided as a score that represents alikelihood of the property being damaged by a wildfire (e.g., burnt). Insome implementations, risk assessments provided by the ML system of thepresent disclosure can be used when determining a course of action. Forexample, one or more preemptive mitigation actions can be determined andperformed to reduce the likelihood that the property will be damaged bywildfire. Example mitigations can include, without limitation, brushclearing, vegetation thinning, and the like.

To provide further context for the subject matter of the presentdisclosure, and as introduced above, ML has been leveraged to generatepredictions around geospatial contexts. For example, ML models can beused to generate predictions representative of characteristics of anatural disaster, such as likelihood of occurrence, duration, severity,spread, among other characteristics, of the natural disaster. Thenon-limiting example natural disaster used to illustrate implementationsof the present disclosure includes wildfires.

In general, ML models can be trained to predict characteristics of anatural disaster using training data that is representative ofcharacteristics of occurrences of the natural disaster, for example.Example types of ML models can include Gradient Boosted Decision Trees(GBDTs), Convolutional Neural Networks (CNNs), Residual Neural Networks(RNNs), and Generative Adversarial Networks (GANs). In general, thetraining data can include region data representative of properties ofrespective regions (e.g., geographical areas), at which the naturaldisaster has occurred. In some examples, each ML model predicts arespective characteristic of the natural disaster. Example ML models caninclude, without limitation, risk models that predict a likelihood ofoccurrence of the natural disaster. Other example ML models can includespread models that predict rates of spread of the natural disaster, ifoccurring, spread models that predict spreads of the natural disaster inthe region, if occurring, and intensity models that predict an intensityof the natural disaster, if occurring. Characteristics of a naturaldisaster can be temporal. For example, a risk of wildfire is higherduring a dry season than during a rainy season. Consequently, each MLmodel can be temporal. That is, for example, each ML model can betrained using training data representative of a particular period oftime. In addition, because the layers of training data all relate to thesame region and contain measurements made at or around the same time, asingle ML model can learn temporal properties from the training data.

In further detail, the region data can include an image of the regionand a set of properties of the region (e.g., provided in a set ofpixel-level data). More generally, the region data can be described as aset of data layers (e.g., N data layers), each data layer providing arespective type of data representative of a property of the region. Insome examples, the data layers can number in the tens of data layers tohundreds of data layers. In some examples, each data layer includes anarray of pixels, each pixel representing a portion of the region andhaving data associated therewith that is representative of the portionof the region. A pixel can represent a portion as an area (e.g., cm²,m², km²) within the region. The area that a pixel represents in one datalayer can be different from the area that a pixel represents in anotherdata layer. For example, each pixel within a first data layer canrepresent X km² and each pixel within a second data layer can representY km², where X≠Y.

An example data layer can include an image layer, in which each pixel isassociated with image data, such as red, green, blue (RGB) values (e.g.,each value ranging from 0 to 255). Another example layer can include avegetation layer, in which, for each pixel, a normalized vegetationdifference index (NVDI) value (e.g., in range of [−1, 1], lower valuesindicating absence of vegetation). Other example layers can include,without limitation, a temperature layer, in which a temperature value isassigned to each pixel, a humidity layer, in which a humidity value isassigned to each pixel, a wind layer, in which wind-related values(e.g., speed, direction) are assigned to each pixel, a barometricpressure layer, in which a barometric pressure value is assigned to eachpixel, a precipitation layer, in which a precipitation value is assignedto each pixel, and an elevation layer, in which an elevation value isassigned to each pixel.

In general, data values for pixels of data layers can be obtained fromvarious data sources including data sources provided by, for example,governmental entities, non-governmental entities, public institutions,and private enterprises. For example, data can be obtained fromdatabases maintained by the National Weather Service (NWS), the UnitedStates Fire Service (USFS), and the California Department of Forestryand Fire Protection (CAL FIRE), among many other entities. For example,weather-related data for a region can be obtained from a web-accessibledatabase (e.g., through a hypertext transfer protocol (HTTP), calls toan application programming interface (API)). In another example, datastored in a relational database can be retrieved through queries to thedatabase (e.g., structured query language (SQL) queries).

Because values across the data layers can change over time, the regiondata can be temporal. For example, temperature values for the region canbe significantly different in summer as compared to winter. Therefore, adata layer represents a characteristic of the region at a particulartime point, and multiple data layers can represent the samecharacteristic in the same region, but at different time points.

Accordingly, the region data can include an array of N data layers(e.g., [1₁, 1₂, . . . , 1_(N)]). Each layer, 1_(i), can include an arrayof pixels, [p_(1,1), p_(i,j)], where each pixel contains a value for aproperty (e.g., vegetation, humidity, wind speed, etc.) at a point inspace. Each layer can also include metadata describing the layer. Forexample, the metadata, M, can include the time T at which thecharacteristic was measures, points indicating the boundaries of theregion, and the size of a pixel: [T, (x₁, y₁), (x₂, y₂), S], where T isthe measurement time, (x₁, y₁) and (x₂, y₂) demarcate the regionboundary, and S indicates the size of the pixel (e.g., m²). Therefore, afull representation of a layer k can be represented as l_(k)=[p_(1,1), .. . , p_(i,j),M].

As training data, the region data, which can be referred to as regiontraining data in the context of training, can include one or morecharacteristic layers that provides known characteristic data forrespective characteristics of a natural disaster. The knowncharacteristic data represents actual values of the respectivecharacteristics prior to occurrence of the natural disaster and as aresult of the natural disaster. For example, a wildfire can occur withina region and, as a result, characteristics of intensity, spread,duration, and the like can be determined for the natural disaster.Accordingly, as training data, the region data can include, for example,p_(i,j)=[l₁, l₂, . . . , C_(A,i,j) ^(K), C_(B,i,j) ^(K), . . . ], wherel_(i) are data layers and C_(A,i,j) ^(K) and C_(A,i,j) ^(K) arerespective known (K) characteristics (i.e., historical characteristics)of a natural disaster of a particular type (A) in question, where typesof natural disasters can include wildfires, floods, hurricanes, etc.

As noted above, ML models are trained using the training data. Thetraining process can depend on a type of the ML model. In general, theML model is iteratively trained, where, during an iteration, alsoreferred to as epoch, one or more parameters of the ML model areadjusted, and an output (e.g., predicted characteristic value) isgenerated based on the training data. For each iteration, a loss valueis determined based on a loss function. The loss value represents adegree of inaccuracy of the output of the ML model as compared to adesired or known value (e.g., known characteristic). The loss value canbe described as a representation of a degree of difference between theoutput of the ML model and the desired output, of the ML model for aparticular example. The desired output for a training example can beincluded in the training example. Examples of loss functions can includemean squared error (MSE) and log loss.

In some examples, if the loss value does not meet an expected value(e.g., is not equal to zero), parameters of the ML model are adjusted(e.g., using backpropagation) in another iteration (epoch) of training.In some examples, the iterative training continues for a pre-definednumber of iterations (epochs). In some examples, the iterative trainingcontinues until the loss value meets the expected value or is within athreshold range of the expected value.

To generate predictions, layers of region data representative of aregion, for which predictions are to be generated, are provided as inputto a (trained) ML model, which generates a predicted characteristic foreach pixel within the region data. An example output of the ML model caninclude p_(i,j)=[C_(i,j) ^(P)], where C is a characteristic predicted(P) by the ML model. In some implementations, a model can make multiplepredictions. For example, if the model makes k predictions, the outputcan be a vector of predictions, [p_(1,i,j), p_(2,i,j), . . . ,p_(k,i,j)]. Example characteristics can include, without limitation,likelihood of occurrence (e.g., risk), a rate of spread, an intensity,and a duration. In some examples, an image of the region can bedisplayed to visually depict the predicted characteristic across theregion. For example, different values of the characteristic can beassociated with respective visual cues (e.g., colors, shades of colors),and the predicted characteristic can be visually displayed as a heatmapover an image of the region.

FIG. 1 illustrates a risk assessment system 100 in accordance withimplementations of the present disclosure. In the example of FIG. 1 ,the risk assessment system 100 includes a parcel-level ML model 102 a, arisk-level ML model 102 b, a landscape-level ML model 102 c, and afusion network 104. As described in further detail herein, theparcel-level ML model 102 a processes parcel-level images 110 a having arelatively small image scale (e.g., 1:50-1:500, measured in cm) and arelatively high image resolution (e.g., 10 cm-5 m per pixel), theneighborhood-level ML model 102 b processes neighborhood-level imageshaving a relatively moderate image scale (e.g., 1:1500-1:2500, measuredin cm) and a relatively moderate image resolution (e.g., 10 m-30 m perpixel), and the landscape-level ML model 102 c processes landscape-levelimages having a relatively large image scale (e.g., 1:10,000-1:30,000,measured in cm) and a relatively low image resolution (e.g., ≥60 m perpixel). In some examples, the parcel-level ML model 102 a provides aparcel-level embedding that encodes a spatial context and resolution ofa parcel-level image 110 a, the neighborhood-level ML model 102 bprovides a neighborhood-level embedding that encodes a spatial contextand resolution of a neighborhood-level image 110 b, and thelandscape-level ML model 102 c provides a landscape-level embedding thatencodes a spatial context and resolution of a landscape-level image 110c.

In some examples, the parcel-level embedding, the neighborhood-levelembedding, and the landscape-level embedding are provided as input tothe fusion network 104. The fusion network 104 processes theparcel-level embedding, the neighborhood-level embedding, and thelandscape-level embedding to provide a risk assessment, represented by ascore 112. In the non-limiting example of the present disclosure, therisk assessment is provided as the score, which represents a likelihoodof a parcel being damaged by a wildfire. For example, and as describedin further detail herein, the parcel-level image 110 a depicts theparcel, and data representative of the parcel itself, theneighborhood-level image 110 b depicts the parcel and a neighborhoodaround the parcel, and data representative of the neighborhood, and thelandscape-level image 110 c depicts the parcel and a landscape aroundthe parcel, and data representative of the landscape. As also describedin further detail herein, the parcel-level image 110 a is at a firstimage scale and a first image resolution, the neighborhood-level image110 b is at a second image scale and a second image resolution, and thelandscape-level image 110 c is at a third image scale and a third imageresolution. The first image scale is larger (more detail) than thesecond image scale, and the second image scale is higher (more detail)than the third image scale. The first image resolution being higher(more granular) than the second image resolution, and the second imageresolution being higher (more granular) than the third image resolution.

In some examples, the parcel-level image 110 a can be provided as astreet-view image. In general, a street-view image of a parcel canresult in access to unique features of a parcel that are not otherwiseavailable using other imaging data. Such features can include, forexample, features that reflect a current state of a parcel (e.g., vinesgrowing on a side of a house, location of cars parked in a driveway). Insome examples, a street-view image can be generated by a device local tothe parcel (e.g., a smartphone, a camera). For example, a user thatrequests a risk assessment for a parcel can capture a street-view imageof the parcel using a smartphone.

In some examples, the parcel-level image 110 a, the neighborhood-levelimage 110 b, and the landscape-level image 110 c can each be provided asoverhead images (e.g., aerial image, satellite image). In some examples,overhead images include any images capturing a geographical region andproviding information for the geographical region. Information for thegeographical region can include, for example, information (e.g.,structures, vegetation, terrain, weather) about one or more parcels(e.g., including structures) located in the geographical region.Overhead images can be, for example, Landsat images, or otherappropriate forms of overhead imagery. The overhead images can be, forexample, RGB images or hyperspectral images. Overhead images can becaptured using satellite technology (e.g., Landsat) or drone technology.In some implementations, overhead images can be captured using anyappropriate high-altitude technology (e.g., drones, weather balloons,planes). In some examples, synthetic aperture radar (SAR) images can beutilized.

In some implementations, overhead images may be captured utilizingradar-based imaging, for example, LIDAR images, RADAR images, or anothertype of imaging using the electromagnetic spectrum., or a combinationthereof. Overhead images can include images of geographic regionsincluding various natural features including different terrains,vegetation, bodies of water, and other features. Satellite/aerial imagescan include images of man-made developments (e.g., housing construction,roads, dams, retaining walls).

In some implementations, the parcel-level ML model 102 a, the risk-levelML model 102 b, the landscape-level ML model 102 c are each provided asa respective CNN. In some examples, the parcel-level ML model 102 a, therisk-level ML model 102 b, the landscape-level ML model 102 c outputrespective feature embeddings from a non-final layer. In someimplementations, the fusion network 104 is provided as a CNN thatoutputs the score 112 from a final layer. In some examples, the finallayer can include a sigmoid activation function that outputs a binaryprediction (e.g., 0, 1) as a classification problem (e.g., 0 indicatesno damage, 1 indicates damage). In some examples, a calibration curvecan be fit to a test set and/or validation set of the fusion network 104during post-training testing and validation. The calibration curve canbe used during inference to convert a relative risk prediction output bythe last layer of the fusion network 102 into a probability thatrepresents an actual probability of damage.

In some implementations, the parcel-level ML model 102 a, the risk-levelML model 102 b, the landscape-level ML model 102 c, and the fusionnetwork 104 are trained during a training phase using a set of trainingdata. In some examples, the set of training data includes sets of imagesassociated with respective parcels and one or more labels for theparcels. In some examples, the one or more labels represent groundtruths in terms of risk for the property represented in a set of images.For example, a set of images can include a parcel-level image andassociated parcel-level data, a neighborhood-level image and associatedneighborhood-level data, and a landscape-level image and associatedlandscape-level data for a property, and a label can indicate whetherthe property represented in the set of images suffered damage from awildfire. For example, the label can be provided as a set of groundtruth images that depict a burn scar resulting from a wildfire at eachof the parcel-level, the neighborhood-level, and the landscape-level,respectively. In some examples, the associated data includes pixel-levelvalues for a property of features depicted in the image (e.g.,vegetation, humidity, wind speed, weather, structure, material,elevation, slope).

Further detail on training data and generating training data isdescribed in commonly assigned U.S. application Ser. No. 17/158,585,filed on Jan. 26, 2021, which is expressly incorporated herein byreference in the entirety for all purposes.

During training, each set of images and associated data are provided asinput to the risk assessment system 100 and a respective score isdetermined. Loss is determined based on the scores and parcels of eachof the parcel-level ML model 102 a, the risk-level ML model 102 b, thelandscape-level ML model 102 c, and the fusion network 104 areiteratively updated (e.g., using backpropagation) until the lossachieves a value (e.g., is minimized). After training, during aninference phase, sets of images representative of parcels are input tothe risk assessment system 100 to provide scores 112 for respectiveparcels. Each score 112 represents a risk assessment for a respectiveparcel in terms of likelihood that the parcel will be damaged by awildfire (e.g., be burnt in a wildfire).

FIGS. 2A-2C depict images 202, 204, 206 of an example property inaccordance with implementations of the present disclosure.

With reference to FIG. 2A, the image 200 depicts a parcel and featureslocated within the parcel (e.g., structure, vegetation). As such, theimage 200 is provided as a parcel-level image having a first image scale(e.g., 1:100) and a first image resolution (e.g., 10 cm per pixel). Theimage 200 is associated with pixel-level data 206, which can include aset of properties (e.g., vegetation, humidity, wind speed, etc.) foreach pixel (e.g., layers) in the image 200. In some examples, the image200 and/or the pixel-level data 206 represent street-view imagery oroverhead imagery for the parcel. In the example of FIG. 2A, the parceldepicted within the image 200 is a relatively small area (e.g.,approximately the size of a single structure and immediatesurroundings). As such, the pixel-level data 206 can have a first datadensity describing the features located on the parcel at a relativelyhigh data density.

With reference to FIG. 2B, the image 202 depicts the parcel and featureslocated within a neighborhood of the parcel (e.g., structures,vegetations). As such, the image 202 is provided as a neighborhood-levelimage having a second image scale (e.g., 1:2000) and a second imageresolution (e.g., 20 m per pixel). The image 202 is associated withpixel-level data 208, which can include a set of properties (e.g.,vegetation, humidity, wind speed, etc.) for each pixel (e.g., layers) inthe image 202. In some examples, the image 204 and/or the pixel-leveldata 208 represent a vegetation segmentation map (e.g., treesegmentation map) for the neighborhood. In the example of FIG. 2B, theparcel depicted within the image 202 is a relatively moderately sizedarea (e.g., a neighborhood around the parcel). As such, the pixel-leveldata 208 can have a second data density describing the features locatedin the neighborhood at a relatively moderate data density.

With reference to FIG. 2C, the image 204 depicts the parcel and featureslocated within a landscape that the parcel is located in (e.g.,structures, vegetations). As such, the image 204 is provided as alandscape-level image having a third image scale (e.g., 1:20000) and athird image resolution (e.g., 60 m per pixel). The image 204 isassociated with pixel-level data 210, which can include a set ofproperties (e.g., vegetation, humidity, wind speed, etc.) for each pixel(e.g., layers) in the image 204. In some examples, the image 204 and/orthe pixel-level data 210 represents elevation maps for the landscape. Inthe example of FIG. 2C, the parcel depicted within the image 204 is arelatively large area (e.g., a landscape that the parcel is located in).As such, the pixel-level data 210 can have a third data densitydescribing the features located in the landscape at a relatively lowdata density.

In some implementations, the risk assessment can be provided from therisk assessment system of the present disclosure in response toreceiving a request for a risk assessment. In some examples, the requestindicates a particular parcel and an adverse event (e.g., a hazard eventsuch as wildfire, flood) that the request is for. In some examples, theparticular parcel can be indicated by a street address and/or GPScoordinates. In some implementations, a set of images and sets ofpixel-level data can be obtained for the parcel. For example, the set ofimages and/or the sets of pixel-level data can be obtained from one ormore databases by submitting a query to the one or more databasesindicating the parcel. In some examples, a parcel-level image can beprovided as a street-level image that is received from a user with arequest for a risk assessment of the parcel depicted in the parcel-levelimage. In some examples, the set of images includes a parcel-levelimage, a neighborhood-level image, and a landscape-level image. In someexamples, the sets of pixel-level data include a set of pixel-level dataproviding feature properties for pixels in the parcel-level data, a setof pixel-level data providing feature properties for pixels in theneighborhood-level data, and a set of pixel-level data providing featureproperties for pixels in the landscape-level data.

In some examples, images in the set of images and sets of pixel-leveldata correspond to a designated time. In some examples, the designatedtime is a time that the request is received. In some examples, thedesignated time is a time indicated in the request. In some examples,the images in the set of images and sets of pixel-level data correspondto the designated time, if a respective time of each image andrespective times of each set of pixel-level data is within a thresholdtime of the designated time (e.g., one month, 3 months). That is, theimages in the set of images and the sets of pixel-level data should berepresentative of the parcel, neighborhood, and landscape at thedesignated time, for which the risk assessment is requested. Forexample, if the designated time is June, images and/or sets ofpixel-level data from December are not representative of the condition(e.g., weather, vegetation) of the parcel, neighborhood, and landscape.Consequently, the risk assessment would be inaccurate. As anotherexample, if the designated time is June, images and/or sets ofpixel-level data from June (e.g., of the same year or a previous year)are representative of the condition (e.g., weather, vegetation) of theparcel, neighborhood, and landscape. Consequently, the risk assessmentwould be accurate.

In some implementations, additional data, such as public records (e.g.,construction year, set back, variance, etc.) and other relevantgeospatial information (e.g., neighborhood housing density, distances tofire stations/emergency services, distances to major roads, etc.) can beprovided for the parcel. In some examples, the additional data can beused to extract features that are relevant for assessing risk to theparcel.

FIG. 3 is a flow diagram of an example process in accordance withimplementations of the present disclosure. Operations of the process 300can be implemented as instructions stored on one or more computerreadable media which may be non-transitory, and execution of theinstructions by one or more data processing apparatus can cause the oneor more data processing apparatus to perform the operations of theprocess 300.

Parcel-level image and parcel-level data, neighborhood-level image andneighborhood-level data, and landscape-level image and landscape-leveldata are received (302). For example, and as described herein, a requestcan be received that indicates a particular parcel and an adverse event(e.g., a hazard event such as wildfire, flood) that the request is for.In response to the request, set of images and/or the sets of pixel-leveldata can be obtained from one or more databases by submitting a query tothe one or more databases indicating the parcel. In some examples, thequery can indicate a designated time that is relevant for the riskassessment. In some examples, a parcel-level image is provided from auser with a request for a risk assessment of the parcel depicted in theparcel-level image.

Parcel-level feature embedding, neighborhood-level feature embedding,and landscape-level feature embedding are generated (304). For example,and as described herein, the parcel-level image (and associatedpixel-level data) can be process by a parcel-level ML model, whichoutputs the parcel-level feature embedding. The parcel-level featureembedding encodes spatial context and resolution at the parcel-level(granular level). As also described herein, the neighborhood-level image(and associated pixel-level data) can be process by a neighborhood-levelML model, which outputs the neighborhood-level feature embedding. Theneighborhood-level feature embedding encodes spatial context andresolution at the neighborhood-level (less granular level). As alsodescribed herein, the landscape-level image (and associated pixel-leveldata) can be process by a landscape-level ML model, which outputs thelandscape-level feature embedding. The landscape-level feature embeddingencodes spatial context and resolution at the landscape-level (leastgranular level).

The parcel-level feature embedding, the neighborhood-level featureembedding, and the landscape-level feature embedding are processed(306). For example, and as described herein, the parcel-level featureembedding, the neighborhood-level feature embedding, and thelandscape-level feature embedding are provided as input to a fusionnetwork, which processes the parcel-level feature embedding, theneighborhood-level feature embedding, and the landscape-level featureembedding collectively.

A risk assessment is provided for the parcel (308). For example, and asdescribed herein, a score is provided that indicates a likelihood thatthe parcel will be damaged in the adverse event. In some examples, thefusion network provides the score. In some examples, a calibration curvethat is fit to a test set and/or validation set of the fusion networkduring post-training testing and validation is used to determine thescore. For example, the calibration curve can be used during inferenceto convert a relative risk prediction output by the last layer of thefusion network into the score. In some examples, the calibration curveis included in the fusion network.

As described herein, the risk assessment system of the presentdisclosure provides one or more technical advantages. In one example,the risk assessment system of the present disclosure enables riskassessments to account for features at different scales in a time- andresource-efficient manner improving over traditional risk assessmentsystems. For example, implementations of the present disclosure provideprocessor- and memory-efficiencies in both training and inference overtraditional risk assessment systems that use techniques such as pyramidnetworks, which are processor- and memory-intensive. For example,implementations of the present disclosure use separate inputs toseparate models. Further, implementations of the present disclosureavoid additional up/down sampling steps that are executed in pyramidnetworks to adjust input resolution to a common grid.

The term “configured” can be used in connection with systems andcomputer program components. For a system of one or more computers to beconfigured to perform particular operations or actions means that thesystem has installed thereon software, firmware, hardware, or acombination thereof that, in operation, cause the system to perform theoperations or actions. For one or more computer programs to beconfigured to perform particular operations or actions means that theone or more programs include instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the operations oractions.

Implementations of the subject matter and the functional operationsdescribed in this specification can be realized in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Implementations of the subject matter described inthis specification can be implemented as one or more computer programs(i.e., one or more modules of computer program instructions) encoded ona tangible non-transitory storage medium for execution by, or to controlthe operation of, data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them. The program instructions can be encoded on anartificially-generated propagated signal (e.g., a machine-generatedelectrical, optical, or electromagnetic signal) that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus.

The term “data processing apparatus” refers to data processing hardwareand encompasses all kinds of apparatus, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, or multiple processors or computers. The apparatus can alsobe, or further include, special purpose logic circuitry (e.g., an FPGA(field programmable gate array) or an ASIC (application-specificintegrated circuit)). The apparatus can optionally include, in additionto hardware, code that creates an execution environment for computerprograms (e.g., code) that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program, which may also be referred to or described as aprogram, software, a software application, an app, a module, a softwaremodule, a script, or code, can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages; and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A program may, but neednot, correspond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document) in a single file dedicatedto the program in question, or in multiple coordinated files (e.g.,files that store one or more modules, sub-programs, or portions ofcode). A computer program can be deployed to be executed on one computeror on multiple computers that are located at one site or distributedacross multiple sites and interconnected by a data communicationnetwork.

In this specification the term “engine” is used broadly to refer to asoftware-based system, subsystem, or process that is programmed toperform one or more specific functions. Generally, an engine will beimplemented as one or more software modules or components, installed onone or more computers in one or more locations. In some cases, one ormore computers will be dedicated to a particular engine; in some cases,multiple engines can be installed and running on the same computer orcomputers.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby special purpose logic circuitry (e.g., a FPGA, an ASIC), or by acombination of special purpose logic circuitry and one or moreprogrammed computers.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors or both, or any other kindof central processing unit. Generally, a central processing unit willreceive instructions and data from a read-only memory or a random accessmemory or both. The essential elements of a computer are a centralprocessing unit for performing or executing instructions and one or morememory devices for storing instructions and data. The central processingunit and the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data (e.g., magnetic,magneto-optical disks, or optical disks). However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice (e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver), or a portable storage device (e.g., a universalserial bus (USB) flash drive) to name just a few.

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks(e.g., internal hard disks or removable disks), magneto-optical disks,and CD-ROM and DVD-ROM disks.

[0001] To provide for interaction with a user, implementations of thesubject matter described in this specification can be provisioned on acomputer having a display device (e.g., a CRT (cathode ray tube) or LCD(liquid crystal display) monitor) for displaying information to the userand a keyboard and a pointing device (e.g., a mouse, a trackball), bywhich the user can provide input to the computer. Other kinds of devicescan be used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback (e.g.,visual feedback, auditory feedback, tactile feedback); and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's device in response to requests received from the web browser.Also, a computer can interact with a user by sending text messages orother forms of message to a personal device (e.g., a smartphone that isrunning a messaging application), and receiving responsive messages fromthe user in return.

Data processing apparatus for implementing machine learning models canalso include, for example, special-purpose hardware accelerator unitsfor processing common and compute-intensive parts of machine learningtraining or production (i.e., inference, workloads).

Machine learning models can be implemented and deployed using a machinelearning framework (e.g., a TensorFlow framework, a Microsoft CognitiveToolkit framework, an Apache Singa framework, an Apache MXNetframework).

[0002] Implementations of the subject matter described in thisspecification can be realized in a computing system that includes aback-end component (e.g., as a data server) a middleware component(e.g., an application server), and/or a front-end component (e.g., aclient computer having a graphical user interface, a web browser, or anapp through which a user can interact with implementations of thesubject matter described in this specification, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication (e.g., a communication network). Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN) (e.g., the Internet).

[0003] The computing system can include clients and servers. A clientand server are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to a userdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the device), which acts as a client.Data generated at the user device (e.g., a result of the userinteraction) can be received at the server from the device.

[0004] While this specification contains many specific implementationdetails, these should not be construed as limitations on the scope ofany invention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particularimplementations of particular inventions. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented in combination in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially be claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and recited inthe claims in a particular order, this should not be understood asrequiring that such operations be performed in the particular ordershown or in sequential order, or that all illustrated operations beperformed, to achieve desirable results. In certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system modules and components in theimplementations described above should not be understood as requiringsuch separation in all implementations, and it should be understood thatthe described program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts.

Particular implementations of the subject matter have been described.Other implementations are within the scope of the following claims. Forexample, the actions recited in the claims can be performed in adifferent order and still achieve desirable results. As one example, theprocesses depicted in the accompanying figures do not necessarilyrequire the particular order shown, or sequential order, to achievedesirable results. In some cases, multitasking and parallel processingmay be advantageous.

What is claimed is:
 1. A method performed by one or more processors, themethod comprising: receiving a request for a risk assessment for aparcel; receiving a set of images for the parcel, the set of imagescomprising two or more images, each image having an image scale and animage resolution that is different from other images in the set ofimages; providing a first-level feature embedding and a second-levelfeature embedding, the first-level feature embedding being provided byprocessing a first-level image through a first-level machine learning(ML) model, and the second-level feature embedding being provided byprocessing a second-level image through a second-level ML model;determining a risk assessment at least partially by processing each ofthe first-level feature embedding and a second-level feature embeddingthrough a fusion network; and providing a representation of the riskassessment for display.
 2. The method of claim 1, wherein thefirst-level feature embedding comprises a parcel-level feature embeddingthat is generated at least partially based on a parcel-level image inthe set of images, and the second-level feature embedding comprises oneof a neighborhood-level feature embedding that is generated at leastpartially based on a neighborhood-level image in the set of images and alandscape-level feature embedding that is generated at least partiallybased on a landscape-level image in the set of images.
 3. The method ofclaim 2, wherein the parcel-level image has an image scale and an imageresolution that are greater than an image scale and the image resolutionof each of the neighborhood-level image and the landscape-level image.4. The method of claim 1, wherein each image in the set of imagesrepresents the parcel within a threshold of time from a designated timeassociated with the request.
 5. The method of claim 1, wherein at leastone image in the set of images comprises an overhead view of the parceland at least one other image in the set of images comprises one of avegetation segmentation map and an elevation map.
 6. The method of claim1, further comprising providing a third-level feature embedding byprocessing a third-level image through a third-level ML model, whereinthe third-level feature embedding is processed through the fusionnetwork in determining the risk assessment.
 7. The method of claim 1,wherein determining the risk assessment comprises converting a relativerisk prediction to the risk assessment using a calibration curve.
 8. Themethod of claim 7, wherein the relative risk prediction is output by alast layer of the fusion network.
 9. The method of claim 1, wherein thefirst-level feature embedding is output from a non-final layer of thefirst-level ML model and the second-level feature embedding is outputfrom a non-final layer of the second-level ML model.
 10. The method ofclaim 1, wherein the first-level feature embedding is further providedby processing a set of first-level pixel data through the first-level MLmodel, and the second-level feature embedding is further provided byprocessing a set of second-level pixel data through the second-level MLmodel.
 11. A non-transitory computer storage medium encoded with acomputer program, the computer program comprising instructions that whenexecuted by a data processing apparatus cause the data processingapparatus to perform operations comprising: receiving a request for arisk assessment for a parcel; receiving a set of images for the parcel,the set of images comprising two or more images, each image having animage scale and an image resolution that is different from other imagesin the set of images; providing a first-level feature embedding and asecond-level feature embedding, the first-level feature embedding beingprovided by processing a first-level image through a first-level machinelearning (ML) model, and the second-level feature embedding beingprovided by processing a second-level image through a second-level MLmodel; determining a risk assessment at least partially by processingeach of the first-level feature embedding and a second-level featureembedding through a fusion network; and providing a representation ofthe risk assessment for display.
 12. The non-transitory computer storagemedium of claim 11, wherein the first-level feature embedding comprisesa parcel-level feature embedding that is generated at least partiallybased on a parcel-level image in the set of images, and the second-levelfeature embedding comprises one of a neighborhood-level featureembedding that is generated at least partially based on aneighborhood-level image in the set of images and a landscape-levelfeature embedding that is generated at least partially based on alandscape-level image in the set of images.
 13. The non-transitorycomputer storage medium of claim 12, wherein the parcel-level image hasan image scale and an image resolution that are greater than an imagescale and the image resolution of each of the neighborhood-level imageand the landscape-level image.
 14. The non-transitory computer storagemedium of claim 11, wherein each image in the set of images representsthe parcel within a threshold of time from a designated time associatedwith the request.
 15. The non-transitory computer storage medium ofclaim 11, wherein at least one image in the set of images comprises anoverhead view of the parcel and at least one other image in the set ofimages comprises one of a vegetation segmentation map and an elevationmap.
 16. The non-transitory computer storage medium of claim 11, whereinoperations further comprise providing a third-level feature embedding byprocessing a third-level image through a third-level ML model, whereinthe third-level feature embedding is processed through the fusionnetwork in determining the risk assessment.
 17. The non-transitorycomputer storage medium of claim 11, wherein determining the riskassessment comprises converting a relative risk prediction to the riskassessment using a calibration curve.
 18. The non-transitory computerstorage medium of claim 17, wherein the relative risk prediction isoutput by a last layer of the fusion network.
 19. The non-transitorycomputer storage medium of claim 11, wherein the first-level featureembedding is output from a non-final layer of the first-level ML modeland the second-level feature embedding is output from a non-final layerof the second-level ML model.
 20. The non-transitory computer storagemedium of claim 11, wherein the first-level feature embedding is furtherprovided by processing a set of first-level pixel data through thefirst-level ML model, and the second-level feature embedding is furtherprovided by processing a set of second-level pixel data through thesecond-level ML model.
 21. A system, comprising: a computing device; anda computer-readable storage device coupled to the computing device andhaving instructions stored thereon which, when executed by the computingdevice, cause the computing device to perform operations comprising:receiving a request for a risk assessment for a parcel; receiving a setof images for the parcel, the set of images comprising two or moreimages, each image having an image scale and an image resolution that isdifferent from other images in the set of images; providing afirst-level feature embedding and a second-level feature embedding, thefirst-level feature embedding being provided by processing a first-levelimage through a first-level machine learning (ML) model, and thesecond-level feature embedding being provided by processing asecond-level image through a second-level ML model; determining a riskassessment at least partially by processing each of the first-levelfeature embedding and a second-level feature embedding through a fusionnetwork; and providing a representation of the risk assessment fordisplay.
 22. The system of claim 21, wherein the first-level featureembedding comprises a parcel-level feature embedding that is generatedat least partially based on a parcel-level image in the set of images,and the second-level feature embedding comprises one of aneighborhood-level feature embedding that is generated at leastpartially based on a neighborhood-level image in the set of images and alandscape-level feature embedding that is generated at least partiallybased on a landscape-level image in the set of images.
 23. The system ofclaim 22, wherein the parcel-level image has an image scale and an imageresolution that are greater than an image scale and the image resolutionof each of the neighborhood-level image and the landscape-level image.24. The system of claim 21, wherein each image in the set of imagesrepresents the parcel within a threshold of time from a designated timeassociated with the request.
 25. The system of claim 21, wherein atleast one image in the set of images comprises an overhead view of theparcel and at least one other image in the set of images comprises oneof a vegetation segmentation map and an elevation map.
 26. The system ofclaim 21, wherein operations further comprise providing a third-levelfeature embedding by processing a third-level image through athird-level ML model, wherein the third-level feature embedding isprocessed through the fusion network in determining the risk assessment.27. The system of claim 21, wherein determining the risk assessmentcomprises converting a relative risk prediction to the risk assessmentusing a calibration curve.
 28. The system of claim 27, wherein therelative risk prediction is output by a last layer of the fusionnetwork.
 29. The system of claim 21, wherein the first-level featureembedding is output from a non-final layer of the first-level ML modeland the second-level feature embedding is output from a non-final layerof the second-level ML model.
 30. The system of claim 21, wherein thefirst-level feature embedding is further provided by processing a set offirst-level pixel data through the first-level ML model, and thesecond-level feature embedding is further provided by processing a setof second-level pixel data through the second-level ML model.